Right now, I am shipping about three detached systems per day and around four complete pitch decks per week, with AI agents doing most of the heavy lifting.
Across dev, research, code generation, testing, documentation, analysis and iteration, the AI workload in my environment can hit 3–5 billion tokens per month.
If I routed all of that through commercial premium LLM API pricing, the equivalent burn could easily reach hundreds of thousands of ringgit monthly.
Now scale that into a corporate environment.
Picture an organisation with hundreds or thousands of employees, each running AI agents for research, analysis, reporting, coding, documentation, operations and decision support.
Token consumption scales hard.
At the same time, enterprises have little choice but to deploy AI agents.
Why?
Because lean teams like ours can now ship at a level that used to require hundreds or even thousands of people.
AI is collapsing the productivity gap between small teams and large corporations.
But there is an engineering side to this.
AI productivity does not have to mean a massive inference bill.
In my stack, even at around 3 billion tokens per month, direct AI cost stays around RM100–RM200 monthly.
The difference is not that we use less AI.
It is because we built a Smart Routing layer around the agent pipeline.
Not every inference call needs the biggest, most expensive model.
Simple tasks → lightweight models.
Coding tasks → specialised models.
Complex reasoning → stronger models.
Deterministic validation → detached systems.
Repetitive processing → automation and conventional compute.
The engineering principle is simple:
Burn premium intelligence only when the task actually requires it.
Our detached systems handle validation, calculations, filtering, reconciliation, rule-based decisions and structured processing - none of which need an LLM.
That lets us run AI aggressively without pricing every request like it needs the top-tier model.
For me, the future of Enterprise AI is not just:
“Give every employee an AI agent.”
It has to be:
“Give every employee an AI agent - but build intelligent infrastructure underneath it.”
Because when an organisation has 1,000 or 10,000 AI-enabled employees, the question is no longer whether AI is being used.
The real question becomes:
How much intelligence is the organisation paying for that it never actually needed?
That is why I treat Smart Routing, specialised models and detached systems as more than optimisation.
They are becoming AI cost-control infrastructure.
At enterprise scale, that difference can be worth millions of ringgit per year.
The next phase of AI adoption will not be about owning the most powerful models.
It will be about knowing when not to route a request to them.
#ArtificialIntelligence #AIAgents #EnterpriseAI #SmartRouting #NeuralOps #Automation #DigitalTransformation #AIInfrastructure #LLM #Productivity
